Classical feedback-based optimization matches or exceeds quantum performance in speed and scalability while quantum retains an edge in final solution quality on tested instances.
Learning parameter curves in feedback-based quantum optimization algorithms
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Transferring FALQON parameters from small to large 3-regular graphs improves Max-Cut approximation ratios by enabling larger safe time steps.
citing papers explorer
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Feedback-based quantum optimization and its classical counterpart: quantum advantage and the power of classical algorithms
Classical feedback-based optimization matches or exceeds quantum performance in speed and scalability while quantum retains an edge in final solution quality on tested instances.
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Second-Order FALQON Parameter Transfer for the Max-Cut Problem on 3-Regular Graphs
Transferring FALQON parameters from small to large 3-regular graphs improves Max-Cut approximation ratios by enabling larger safe time steps.